Summary of Benign Overfitting For Regression with Trained Two-layer Relu Networks, by Junhyung Park et al.
Benign Overfitting for Regression with Trained Two-Layer ReLU Networks
by Junhyung Park, Patrick Bloebaum, Shiva Prasad Kasiviswanathan
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers study the least-square regression problem using a two-layer fully-connected neural network with ReLU activation function, trained by gradient flow. The main findings include a generalization result that requires no assumptions on the underlying regression function or noise, and an analysis of benign overfitting for finite-width ReLU networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows that with the right conditions, the neural network can learn to generalize well without assuming anything about the data. This is important because it means we can use these networks in situations where we don’t know much about the underlying patterns. Additionally, the researchers found that the network overfits to the training data, which might seem bad at first but is actually a useful property. |
Keywords
» Artificial intelligence » Generalization » Neural network » Overfitting » Regression » Relu
